Natural language processing (NLP) allows machines to read text or understand human speech and respond with a meaningful reply. It can automate at a previously unachievable level, allowing machines and humans to communicate.
Recent advancements in artificial intelligence (AI) and NLP have enabled new applications for human-to-machine interactions.
Such capabilities have significantly impacted service experiences in recent years. For example, it automates transcriptions of call recordings in real-time, providing contact center agents with meaningful training data.
Thanks to such NLP tools, customers can also interact with conversational interfaces to speed up answers to questions.
Sentiment analysis, though, is perhaps the most exciting feature of NLP. It sorts and understands the nuances in human voices and text, giving call center operations unparalleled insights into customer emotions.
The following video includes many more fascinating insights into the basics of NLP.
Why is NLP Crucial for CX Professionals?
NLP converts unstructured data sets into a structured, formalized format for analytics or as a trigger for automated events.
Through such functionality, contact centers can drive positive customer experiences by:
- Interacting with customers at scale.
- Detecting patterns across massive data sets.
- Organizing large quantities of data.
- Aggregating this information seamlessly in a multi-channel environment.
Customer Experience Use Cases for NLP
Each of the following use cases highlights how contact centers can improve customer experience with NLP:
- Transcribing recorded calls and enabling automated audio captioning in product tutorial videos.
- Running customer feedback analysis from unstructured, descriptive feedback to identify keywords, dominant sentiment, and trends.
- Enabling paperless processing by extracting data from images, PDFs, and screenshots to populate electronic forms and fields (helpful for banking and the public sector).
- Checking for specific keywords in written and telephone communications and automatically triggering actions.
- Supporting self-service and virtual assistants.
- Allowing document classification, sentiment analysis, and knowledge graphs.
Three NLP Libraries to Consider
Three popular NLP libraries fuel NLP innovation. These are:
- SpaCy is a free, open-source library in Python.
- NLTK is a platform for building Python programs to work with human language data.
- GenSim is a Python library for topic modeling, document indexing, and similarity retrieval.
The NLTK library is the most widely used, enabling multiple applications, such as sentiment analysis, classification, and tokenization.
However, GenSim is also an excellent option for text generation, data analysis, and semantic search applications.
Adopting NLP In the Contact Center
A single statement in a human conversation possesses rich data, full of potential meaning and variation. Yet, computer systems often struggle to manage it.
For example, standalone keywords, sentence structure, underlying sentiment, and customer metadata must be organized, structured, and arranged to produce reliable analysis.
Multiply this by thousands of customers speaking to agents on dozens of channels daily. That is a massive volume of data to parse.
Typically, companies lack adequate in-house computing infrastructure and an absence of data scientists.
There are, of course, open-source or commercial NLP libraries available. But building one from scratch is a significant, time-consuming effort. That’s why most companies partner with NLP consulting companies with domain-specific expertise.
Several companies offer advanced tools using NLP for contact centers. These include:
- Chattermill – An AI and NLP-powered feedback analysis solution, including CX automation and sophisticated dashboards.
- Ascribe – A CX analysis and visualization company using Ai and NLP; also offers AI project acceleration solutions.
- Wootric (now part of InMoment)– A customer experience management and analytics software solution powered by NLP, including text and sentiment analytics.
Yet, most leading contact center providers today, like Genesys, NICE, and Talkdesk, incorporate NLP technology into their conversational offerings, making their chatbots more intuitive and accurate.
A Checklist for Implementing NLP
Here is an assessment checklist when considering an NLP application:
- Where do I need your NLP data hosted, on-premises or in the cloud?
- What is the text-to-speech accuracy rate, and what measures will mitigate false positives?
- Are the NLP insights actionable?
- Can I customize the AI model based on industry vocabulary and requirements?
- How does the NLP’s capability integrate with the existing CX solution stack – through ready APIs or native integrations built from scratch?
As NLP and conversational AI tools gradually become part of the service experience, contact centers can gain the technology and insights to combat customer, employee, and business problems.
Fortunately, proven advancements in AI and the availability of open-source libraries will allow users to develop NLP from scratch and right out of the box.